Graph learning is widely recognized as a crucial technique in multi-view
clustering. Existing graph learning methods typically involve constructing an
adaptive neighbor graph based on probabilistic neighbors and then learning a
consensus graph to for clustering, however, they are confronted with two
limitations. Firstly, they often rely on Euclidean distance to measure
similarity when constructing the adaptive neighbor graph, which proves
inadequate in capturing the intrinsic structure among data points in many
real-world scenarios. Secondly, most of these methods focus solely on consensus
graph, ignoring view-specific graph information. In response to the
aforementioned drawbacks, we in this paper propose a novel tensor-based graph
learning framework that simultaneously considers consistency and specificity
for multi-view clustering. Specifically, we calculate the similarity distance
on the Stiefel manifold to preserve the intrinsic structure among data points.
By making an assumption that the learned neighbor graph of each view comprises
both a consistent graph and a view-specific graph, we formulate a new
tensor-based target graph learning paradigm. Owing to the benefits of tensor
singular value decomposition (t-SVD) in uncovering high-order correlations,
this model is capable of achieving a complete understanding of the target
graph. Furthermore, we develop an iterative algorithm to solve the proposed
objective optimization problem. Experiments conducted on real-world datasets
have demonstrated the superior performance of the proposed method over some
state-of-the-art multi-view clustering methods. The source code has been
released on this https URL

本文提出一种新颖的基于张量的图学习框架，同时考虑了多视图聚类的一致性和特异性，通过在斯蒂费尔流形上计算相似性距离，解决了现有方法中使用欧氏距离度量相似性的不足，并通过张量奇异值分解实现对目标图的全面理解。实验证明，该方法在真实数据集上的性能优于一些现有的多视图聚类方法。